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Vertex AI


Developing autonomous driving technology is a battle with data, both from a volume and data format point of view. Sources include point cloud 3D data obtained from LIDAR, video data obtained from multiple cameras, GPS position information, millimeter-wave radar, steering and various sensor information. Even in a busy city, less than 1% of the raw data …

Today, manufacturers are advancing on their factory digitalization journey, betting on innovative technologies to strengthen competitiveness, deliver sustainable growth, and offer new services. Macroeconomic factors – such as high energy costs, increasing labor, and raw material shortages – drive the need for urgent operational optimizations and automation.Cloud capabilities have matured at an accelerated pace, giving …

If organizations can easily analyze unstructured data streams, like live video and images, they can more effectively leverage information from the physical world to create intelligent  business applications. Retailers can improve shelf management by instantly spotting what products are out of stock, manufacturers can  reduce product defects by detecting production errors in real time, and …

Data scientists choose models based on various tradeoffs when solving machine learning (ML) problems that involve tabular (i.e., structured) data, the most common data type within enterprises. Among such models, decision trees are popular because they are easy to interpret, fast to train, and can obtain high accuracy quickly from small-scale datasets. On the other …

Machine learning (ML) is iterative in nature — model improvement is a necessity to drive the best business outcomes. Yet, with the proliferation of model artifacts, it can be difficult to ensure that only the best models make it into production. Data science teams may get access to new training data, expand the scope of …

Volkswagen strives to design beautiful, performant, and energy efficient vehicles. This entails an iterative process where designers go through many design drafts, evaluating each, integrating the feedback, and refining. For example, a vehicle’s drag coefficient—its resistance to air—is one of the most important factors of energy efficiency. Thus, getting estimates of the drag coefficient for …

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to …

“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, we explore how to leverage artificial intelligence (AI) and machine learning (ML) for faster innovation …

You’re working on a new machine learning problem, and the first environment you use is a notebook. Your data is stored on your local machine, and you try out different model architectures and configurations, executing the cells of your notebook manually each time. This workflow is great for experimentation, but you quickly hit a wall …